
"""simple docstring"""



import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


a       :						Optional[int]       		=		logging.getLogger(__name__)

a       :						Any       		=		5_0  # max width of layer names
a       :						Optional[Any]       		=		7_0  # max width of quantizer names


def 			_UpperCamelCase						(    _A   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = parser.add_argument_group("""quant_trainer arguments"""   )
    group.add_argument("""--wprec"""       ,   type=_A       ,   default=8       ,   help="""weight precision"""   )
    group.add_argument("""--aprec"""       ,   type=_A       ,   default=8       ,   help="""activation precision"""   )
    group.add_argument("""--quant-per-tensor"""       ,   action="""store_true"""       ,   help="""per tensor weight scaling"""   )
    group.add_argument("""--quant-disable"""       ,   action="""store_true"""       ,   help="""disable all quantizers"""   )
    group.add_argument("""--quant-disable-embeddings"""       ,   action="""store_true"""       ,   help="""disable all embeddings quantizers"""   )
    group.add_argument("""--quant-disable-keyword"""       ,   type=_A       ,   nargs="""+"""       ,   help="""disable quantizers by keyword"""   )
    group.add_argument("""--quant-disable-layer-module"""       ,   type=_A       ,   help="""disable quantizers by keyword under layer."""   )
    group.add_argument("""--quant-enable-layer-module"""       ,   type=_A       ,   help="""enable quantizers by keyword under layer"""   )
    group.add_argument("""--calibrator"""       ,   default="""max"""       ,   help="""which quantization range calibrator to use"""   )
    group.add_argument("""--percentile"""       ,   default=_A       ,   type=_A       ,   help="""percentile for PercentileCalibrator"""   )
    group.add_argument("""--fuse-qkv"""       ,   action="""store_true"""       ,   help="""use the same scale factor for qkv"""   )
    group.add_argument("""--clip-gelu"""       ,   metavar="""N"""       ,   type=_A       ,   help="""clip gelu output maximum value to N"""   )
    group.add_argument(
        """--recalibrate-weights"""       ,   action="""store_true"""       ,   help=(
            """recalibrate weight amaxes by taking the max of the weights."""
            """ amaxes will be computed with the current quantization granularity (axis)."""
        )       ,   )


def 			_UpperCamelCase						(    _A   )     ->	Optional[int]:





    """simple docstring"""

    if args.calibrator == "max":
        _UpperCAmelCase        = """max"""
    elif args.calibrator == "percentile":
        if args.percentile is None:
            raise ValueError("""Specify --percentile when using percentile calibrator"""   )
        _UpperCAmelCase        = """histogram"""
    elif args.calibrator == "mse":
        _UpperCAmelCase        = """histogram"""
    else:
        raise ValueError(F"""Invalid calibrator {args.calibrator}"""   )

    _UpperCAmelCase        = QuantDescriptor(num_bits=args.aprec       ,   calib_method=_A   )
    _UpperCAmelCase        = QuantDescriptor(num_bits=args.wprec       ,   axis=(None if args.quant_per_tensor else (0,))   )
    quant_nn.QuantLinear.set_default_quant_desc_input(_A   )
    quant_nn.QuantLinear.set_default_quant_desc_weight(_A   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=False       ,   _A=False   )     ->	Tuple:





    """simple docstring"""

    logger.info("""Configuring Model for Quantization"""   )
    logger.info(F"""using quantization package {pytorch_quantization.__file__}"""   )

    if not calib:
        if args.quant_disable_embeddings:
            set_quantizer_by_name(_A       ,   ["""embeddings"""]       ,   which="""weight"""       ,   _disabled=_A   )

        if args.quant_disable:
            set_quantizer_by_name(_A       ,   [""""""]       ,   _disabled=_A   )

        if args.quant_disable_keyword:
            set_quantizer_by_name(_A       ,   args.quant_disable_keyword       ,   _disabled=_A   )

        if args.quant_disable_layer_module:
            set_quantizer_by_name(_A       ,   [R"""layer.\d+.""" + args.quant_disable_layer_module]       ,   _disabled=_A   )

        if args.quant_enable_layer_module:
            set_quantizer_by_name(_A       ,   [R"""layer.\d+.""" + args.quant_enable_layer_module]       ,   _disabled=_A   )

        if args.recalibrate_weights:
            recalibrate_weights(_A   )

        if args.fuse_qkv:
            fuse_qkv(_A       ,   _A   )

    if args.clip_gelu:
        clip_gelu(_A       ,   args.clip_gelu   )

    # if args.local_rank in [-1, 0] and not calib:
    print_quant_summary(_A   )


def 			_UpperCamelCase						(    _A   )     ->	Tuple:





    """simple docstring"""

    logger.info("""Enabling Calibration"""   )
    for name, module in model.named_modules():
        if name.endswith("""_quantizer"""   ):
            if module._calibrator is not None:
                module.disable_quant()
                module.enable_calib()
            else:
                module.disable()
            logger.info(F"""{name:80}: {module}"""   )


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    logger.info("""Loading calibrated amax"""   )
    for name, module in model.named_modules():
        if name.endswith("""_quantizer"""   ):
            if module._calibrator is not None:
                if isinstance(module._calibrator       ,   calib.MaxCalibrator   ):
                    module.load_calib_amax()
                else:
                    module.load_calib_amax("""percentile"""       ,   percentile=args.percentile   )
                module.enable_quant()
                module.disable_calib()
            else:
                module.enable()
    model.cuda()
    print_quant_summary(_A   )


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Dict:





    """simple docstring"""

    def fusea(_A       ,   _A       ,   _A   ):
        for mod in [qq, qk, qv]:
            if not hasattr(_A       ,   """_amax"""   ):
                print("""          WARNING: NO AMAX BUFFER"""   )
                return
        _UpperCAmelCase        = qq._amax.detach().item()
        _UpperCAmelCase        = qk._amax.detach().item()
        _UpperCAmelCase        = qv._amax.detach().item()

        _UpperCAmelCase        = max(_A       ,   _A       ,   _A   )
        qq._amax.fill_(_A   )
        qk._amax.fill_(_A   )
        qv._amax.fill_(_A   )
        logger.info(F"""          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}"""   )

    for name, mod in model.named_modules():
        if name.endswith(""".attention.self"""   ):
            logger.info(F"""FUSE_QKV: {name:{name_width}}"""   )
            fusea(mod.matmul_q_input_quantizer       ,   mod.matmul_k_input_quantizer       ,   mod.matmul_v_input_quantizer   )
            if args.quant_per_tensor:
                fusea(mod.query._weight_quantizer       ,   mod.key._weight_quantizer       ,   mod.value._weight_quantizer   )


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	List[str]:





    """simple docstring"""

    for name, mod in model.named_modules():
        if name.endswith(""".output.dense"""   ) and not name.endswith("""attention.output.dense"""   ):
            _UpperCAmelCase        = mod._input_quantizer._amax.data.detach().item()
            mod._input_quantizer._amax.data.detach().clamp_(max=_A   )
            _UpperCAmelCase        = mod._input_quantizer._amax.data.detach().item()
            logger.info(F"""CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}"""   )


def 			_UpperCamelCase						(    _A   )     ->	Union[str, Any]:





    """simple docstring"""

    for name, mod in model.named_modules():
        if hasattr(_A       ,   """_weight_quantizer"""   ) and mod._weight_quantizer.axis is not None:
            _UpperCAmelCase        = mod.weight.shape[0]
            _UpperCAmelCase        = mod._weight_quantizer._amax.detach()
            _UpperCAmelCase        = torch.ones(_A       ,   dtype=amax.dtype       ,   device=amax.device   ) * amax
            print(F"""expanding {name} {amax} -> {mod._weight_quantizer._amax}"""   )


def 			_UpperCamelCase						(    _A   )     ->	List[Any]:





    """simple docstring"""

    for name, mod in model.named_modules():
        if hasattr(_A       ,   """_weight_quantizer"""   ):
            if not hasattr(mod.weight_quantizer       ,   """_amax"""   ):
                print("""RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER"""   )
                continue

            # determine which axes to reduce across
            # e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
            _UpperCAmelCase        = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis   )
            _UpperCAmelCase        = set(range(len(mod.weight.size()   )   )   ) - axis_set
            _UpperCAmelCase        = pytorch_quantization.utils.reduce_amax(mod.weight       ,   axis=_A       ,   keepdims=_A   ).detach()
            logger.info(F"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"""   )
            _UpperCAmelCase        = amax


def 			_UpperCamelCase						(    _A       ,   _A=2_5       ,   _A=1_8_0       ,   _A=None   )     ->	Dict:





    """simple docstring"""

    if ignore is None:
        _UpperCAmelCase        = []
    elif not isinstance(_A       ,   _A   ):
        _UpperCAmelCase        = [ignore]

    _UpperCAmelCase        = 0
    for name, mod in model.named_modules():
        if not hasattr(_A       ,   """weight"""   ):
            continue
        _UpperCAmelCase        = max(_A       ,   len(_A   )   )

    for name, mod in model.named_modules():
        _UpperCAmelCase        = getattr(_A       ,   """_input_quantizer"""       ,   _A   )
        _UpperCAmelCase        = getattr(_A       ,   """_weight_quantizer"""       ,   _A   )
        if not hasattr(_A       ,   """weight"""   ):
            continue
        if type(_A   ) in ignore:
            continue
        if [True for s in ignore if type(_A   ) is str and s in name]:
            continue
        _UpperCAmelCase        = F"""Act:{input_q.extra_repr()}"""
        _UpperCAmelCase        = F"""Wgt:{weight_q.extra_repr()}"""
        _UpperCAmelCase        = F"""{name:{name_width}} {act_str} {wgt_str}"""
        if len(_A   ) <= line_width:
            logger.info(_A   )
        else:
            logger.info(F"""{name:{name_width}} {act_str}"""   )
            logger.info(F"""{"  ":{name_width}} {wgt_str}"""   )


def 			_UpperCamelCase						(    _A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = 0
    for name, mod in model.named_modules():
        if isinstance(_A       ,   pytorch_quantization.nn.TensorQuantizer   ):
            print(F"""{name:80} {mod}"""   )
            count += 1
    print(F"""{count} TensorQuantizers found in model"""   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = getattr(_A       ,   _A       ,   _A   )
    if quantizer_mod is not None:
        assert hasattr(_A       ,   _A   )
        setattr(_A       ,   _A       ,   _A   )
    else:
        logger.warning(F"""{name} has no {quantizer}"""   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A="both"       ,   **_A   )     ->	List[str]:





    """simple docstring"""

    _UpperCAmelCase        = F"""Warning: changing {which} quantizers of {name:{qname_width}}"""
    for k, v in kwargs.items():
        s += F""" {k}={v}"""
        if which in ["input", "both"]:
            set_quantizer(_A       ,   _A       ,   """_input_quantizer"""       ,   _A       ,   _A   )
        if which in ["weight", "both"]:
            set_quantizer(_A       ,   _A       ,   """_weight_quantizer"""       ,   _A       ,   _A   )
    logger.info(_A   )







def 			_UpperCamelCase						(    _A       ,   _A       ,   **_A   )     ->	Tuple:





    """simple docstring"""

    for name, mod in model.named_modules():
        if hasattr(_A       ,   """_input_quantizer"""   ) or hasattr(_A       ,   """_weight_quantizer"""   ):
            for n in names:
                if re.search(_A       ,   _A   ):
                    set_quantizers(_A       ,   _A       ,   **_A   )
        elif name.endswith("""_quantizer"""   ):
            for n in names:
                if re.search(_A       ,   _A   ):
                    _UpperCAmelCase        = F"""Warning: changing {name:{name_width}}"""
                    for k, v in kwargs.items():
                        s += F""" {k}={v}"""
                        setattr(_A       ,   _A       ,   _A   )
                    logger.info(_A   )

"""simple docstring"""



import cva
import numpy as np




class 		a_    :







    def __init__(      self      :							Optional[Any]  ,       __UpperCamelCase      :							float  ,       __UpperCamelCase      :							int  )		->Dict:






        '''simple docstring'''






        if k in (0.0_4, 0.0_6):
            _UpperCAmelCase        = k
            _UpperCAmelCase        = window_size
        else:
            raise ValueError("""invalid k value"""  )







    def __str__(      self      :							Tuple  )		->str:






        '''simple docstring'''






        return str(self.k  )







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							str  )		->tuple[cva.Mat, list[list[int]]]:






        '''simple docstring'''






        _UpperCAmelCase        = cva.imread(__UpperCamelCase  ,       0  )
        _UpperCAmelCase     ,_UpperCAmelCase        = img.shape
        _UpperCAmelCase        = []
        _UpperCAmelCase        = img.copy()
        _UpperCAmelCase        = cva.cvtColor(__UpperCamelCase  ,       cva.COLOR_GRAY2RGB  )
        _UpperCAmelCase     ,_UpperCAmelCase        = np.gradient(__UpperCamelCase  )
        _UpperCAmelCase        = dx**2
        _UpperCAmelCase        = dy**2
        _UpperCAmelCase        = dx * dy
        _UpperCAmelCase        = 0.0_4
        _UpperCAmelCase        = self.window_size // 2
        for y in range(__UpperCamelCase  ,       h - offset  ):
            for x in range(__UpperCamelCase  ,       w - offset  ):
                _UpperCAmelCase        = ixx[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = iyy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = ixy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()

                _UpperCAmelCase        = (wxx * wyy) - (wxy**2)
                _UpperCAmelCase        = wxx + wyy
                _UpperCAmelCase        = det - k * (trace**2)
                # Can change the value
                if r > 0.5:
                    corner_list.append([x, y, r]  )
                    color_img.itemset((y, x, 0)  ,       0  )
                    color_img.itemset((y, x, 1)  ,       0  )
                    color_img.itemset((y, x, 2)  ,       2_55  )
        return color_img, corner_list


if __name__ == "__main__":
 a       :						List[Any]       		=		HarrisCorner(0.04, 3)
 a		,			a       :						List[Any]       		=		edge_detect.detect('''path_to_image''')
 cva.imwrite('''detect.png''', color_img)